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Öğe Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils(Springer Heidelberg, 2020) Vaheddoost, Babak; Guan, Yiqing; Mohammadi, BabakField capacity (FC) and permanent wilting point (PWP) are two important properties of the soil when the soil moisture is concerned. Since the determination of these parameters is expensive and time-consuming, this study aims to develop and evaluate a new hybrid of artificial neural network model coupled with a whale optimization algorithm (ANN-WOA) as a meta-heuristic optimization tool in defining the FC and the PWP at the basin scale. The simulated results were also compared with other core optimization models of ANN and multilinear regression (MLR). For this aim, a set of 217 soil samples were taken from different regions located across the West and East Azerbaijan provinces in Iran, partially covering four important basins of Lake Urmia, Caspian Sea, Persian Gulf-Oman Sea, and Central-Basin of Iran. Taken samples included portion of clay, sand, and silt together with organic matter, which were used as independent variables to define the FC and the PWP. A 80-20 portion of the randomly selected independent and dependent variable sets were used in calibration and validation of the predefined models. The most accurate predictions for the FC and PWP at the selected stations were obtained by the hybrid ANN-WOA models, and evaluation criteria at the validation phases were obtained as 2.87%, 0.92, and 2.11% respectively for RMSE, R-2, and RRMSE for the FC, and 1.78%, 0.92, and 10.02% respectively for RMSE, R-2, and RRMSE for the PWP. It is concluded that the organic matter is the most important variable in prediction of FC and PWP, while the proposed ANN-WOA model is an efficient approach in defining the FC and the PWP at the basin scale.Öğe Application of Signal Processing in Tracking Meteorological Drought in a Mountainous Region(Birkhauser, 2021) Vaheddoost, Babak; Safari M.J.S.This study addresses the application of signal processing in the evaluation of meteorological drought associated with monthly precipitation time series. Several drought indices and a Haar wavelet decomposition (WD) with ten components are implemented in the evaluation of the monthly precipitation of a mountainous region called Mount Uludag in Turkey. Monthly precipitation time series in three meteorological stations at the summit and foothills are used. The Standardized Precipitation Index (SPI) is used at monthly, annual, and 12- and 48-month moving average time frames as the benchmark to investigate the drought patterns. The results obtained by the WD and SPI are then confirmed using the Z-score index (ZSI) at monthly and annual scales, together with the modified China Z-index (MCZI) and rainfall anomaly index (RAI) at a monthly scale. Changes in the moments of the distribution, correlation analysis, mutual information, and power spectrum are applied to investigate the nature of the relationship between the sequences of precipitation events in time and space. The temporal correlation analysis, together with the mutual information, showed that the system has a short-term memory with strong seasonality. Similarly, the power spectra depicted major seasonality at 1, 3, 5, 6, 12, 22, and 60 months in the precipitation time series. It is concluded that the recent drought events have an infrequent nature, which altered the sinusoidal patterns of the large-scale events. The SPI-48 and the WD showed that declines are strongly related to the large-scale cycles, but the decline patterns are more related to the station located at the mountain summit.Öğe Conceptualization of the indirect link between climate variability and lake water level using conditional heteroscedasticity(Taylor and Francis Ltd., 2021) Fathian F.; Vaheddoost, BabakThis study investigates the indirect effect of large-scale climate oscillations and the corresponding teleconnection with lake water level (WL) oscillations. For this, the effect of the Southern Oscillation Index (SOI), and North Atlantic Oscillation (NAO) on the Lake Urmia WL during 1966–2016 is investigated using cross-correlation, cross-wavelet (XWT), wavelet-coherence (CWT), and nonlinear multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models. Based on the XWT and CWT analyses, a temporal phase of WL leading by the SOI and an anti-phase of WL-NAO linkage within 11 years is evident. The models also depict a long-term persistence and effectiveness of the conditional covariance on both SOI–WL and NAO–WL links. It is concluded that the SOI and NAO have no immediate impact on the WL while the magnitude of the impact is exacerbated after the year 2000; however, the establishment of the SOI–WL link is found to be more relevant than that of the NAO–WL link.Öğe Discharge coefficient for vertical sluice gate under submerged condition using contraction and energy loss coefficients(Elsevier Ltd, 2021) Vaheddoost, Babak; Safari M.J.S.; Ilkhanipour Zeynali R.A novel method is suggested for the determination of flow discharge in vertical sluice gates with considerably small bias. First, in order to derive an equation for the discharge coefficient, energy-momentum equations are implemented to define the physical realization of the phenomenon. Afterward, the discharge coefficient is presented in terms of contraction and energy loss coefficients. Subsequently, discharge coefficient, contraction, and energy loss coefficients were determined through an implicit optimization technique on the data. Data analysis illustrated that there is a meaningful power relationship between the contraction and energy loss coefficients. Thereafter, dimensional analysis is performed and an explicit best-fit regression equation is developed for defining the energy loss coefficient. The obtained equations for contraction and energy loss coefficients were then used in the computation of the discharge coefficient and determination of the flow discharge in the vertical sluice gate. The performance of the developed approach is validated against the selected benchmarks existing in the literature.Öğe Drought indices and indicators revisited(Springer Heidelberg, 2019) Yihdego, Yohannes; Vaheddoost, Babak; Al-Weshah, Radwan A.There are numerous drought indicators used by decision makers all around the globe which have been developed to fulfill specific needs. By far, risks associated with drought and related consequences have become a bold topic for scientists in which debates still taking place everywhere. No global drought indices could provide universally accepted results since almost all of these indices are based on observed data as key performance indicators. In this respect, researchers spend a lot of effort on this issue for a better understanding on the various indices which are proposed until now. It is crucial to get a better sense on how drought can develop and how it can be monitored. It is also important to understand that, recent global challenges like climate change also amplifies the obligation on continues effort toward developing better indicators and methods to monitor droughts. As climate patterns change or a seasonal shift occurs, predefined drought indicators become useless. In this review, the concepts of drought indices and indicators are revisited and evaluated. Pros and cons of frequently used indices are addressed and the major differences between them are bolded. It is concluded that each index is applicable to fulfill expectations of a specific drought type while pre-knowledge about each case is very crucial. However, there is a need to develop a composite drought index to integrate all relevant data and drought definitions, with respect to the dominant types of monthly droughts in time and space together with climate change scenarios.Öğe ENN-SA: A novel neuro-annealing model for multi-station drought prediction(Pergamon-Elsevier Science Ltd, 2020) Mehr, Ali Danandeh; Vaheddoost, Babak; Mohammadi, BabakThis paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales in a meteorology station with lack of data through the intelligent use of SPI series of the nearby stations as the model inputs. The capability of the hybrid model for multi-station prediction of meteorological drought was examined through the cross-validation technique for Kecioren station in Ankara Province, Turkey. To this end, the SPI-3, SPI-6, and SPI-12 at the station were modeled using the same indices of five nearby stations. In the first step, SVM was trained using different kernels in order to generate and classify a set of plausible multi-station prediction scenarios. Then, ENN was used to regress the SPI series at each scenario and finally, the SA component of the integrated model was utilized to improve the ENN efficiency. Various error and complexity measures were used to detect the models' performance. The results showed the ENN-SA is promising and efficient for multi-station SPI prediction.Öğe Identification of the trends associated with the SPI and SPEI indices across Ankara, Turkey(Springer Wien, 2020) Danandeh Mehr, Ali; Vaheddoost, BabakThis study investigates the main characteristics (duration, severity, and trend) of meteorological drought events over Ankara Province, Turkey. We used 46 years of observed monthly precipitation and temperature series from six meteorological stations distributed across the study area to derive the well-known meteorological drought indices; the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) both in 3-, 6-, and 12-month timescales. A comparative analysis between the indices, associated drought events, and potential trends at each station are presented. To explore the drought trends in each station, the well-documented Spearman rank-order correlation coefficient, innovative Sen's method, and innovative trend analysis are applied. The results showed that the province faced five extreme drought events during the period of 1971-2016, although temporal inconsistencies between the SPI and SPEI, particularly in 6- and 12-month timescales exist. Considering the SPEI, the results indicated a slight descending trend in the observed drought events; however, the SPI does not conform to the same pattern.Öğe Interaction of groundwater with Lake Urmia in Iran(Wiley, 2018) Vaheddoost, Babak; Aksoy, HafzullahBeing a large hyper-saline water body, Lake Urmia in north-western Iran deals with a gradual decline in its water level. Most of the studies on Lake Urmia have neglected the groundwater issue. In this study, as a direct approach, the interaction between the groundwater level and the lake water level is investigated both in time and space by analysing the groundwater data compiled from observation wells surrounding the lake. Baseflow separation is considered as an indirect approach to understand the groundwater contribution to the river network flowing into the lake. It is determined that about 70% of run-off in the rivers draining into the lake is born in the form of baseflow. An interaction between the lake and the groundwater storage is clearly seen from the analysis to conclude that groundwater has a potential to recharge the lake. Thus, the shrinkage of water level in Lake Urmia could be expected to accelerate with the drastic use of groundwater, which will be a disaster with no return.Öğe Modeling the volatility changes in Lake Urmia water level time series(Springer Wien, 2021) Fathian, Farshad; Vaheddoost, BabakThe decline in Lake Urmia (LU) water level during the past two decades has been addressed by several studies. However, the conducted studies could not come across a practical solution by considering the sample mean in the lake water level time series. For this, the present study suggests a fresh look to the lake water level decline in LU by addressing the volatility changes instead. The Bayesian change-point detection method was used to define the major and critical change points during the study period from January 1966 to December 2016 on a daily scale. Results indicated that major changes occurred in early 2000, and the time series can be studied under the pre- and post-change point events. Afterward, several methods namely shift-track and mono- and multiple-trend line analyses were used to remove the trends associated with the lake water level time series. The de-trending approaches later were applied separately for the entire study period, before 2000 (i.e., 1966-1999) and afterward (i.e., 2000-2016). Then, the de-trended time series were used, and a generalized autoregressive conditional heteroscedasticity (GARCH) model was fitted to the de-trended time series to predict the volatility changes in the data run. Results indicated to descending and ascending changes, respectively, in short- and long-term persistence after 2000. The GARCH(1,1) model was found to be satisfactory to interpret the pre- and post-turn point events, while the changes in short- and the long-term persistence were calculated as 0.53 to 0.75 and 0.46 to 0.24, respectively. In addition, by considering the lake water level anomaly and coefficient of variation in LU and two neighboring cases of Lake Sevan and Lake Van, it is concluded that the changes are exclusive to LU, and the rate of changes was accelerated after 2006.Öğe Parametric and nonparametric regression models in study of the length of hydraulic jump after a multi-segment sharp-crested V-notch weir(Iwa Publishing, 2020) Saadatnejadgharahassanlou, Hamid; Zeynali, Rasoul Ilkhanipour; Vaheddoost, Babak; Gharehbaghi, AminA multi-segment sharp-crested V-notch weir (SCVW) was used both theoretically and experimentally in this study to evaluate the length of the hydraulic jump at the downstream of the weir. For this aim, a SCVW with three triangular segments at different tail-water depths (tailgate angles), and ten different discharges at a steady flow condition were investigated. Then, the most effective parameters on the length of the hydraulic jump are defined and several parametric and nonparametric regression models, namely multi-linear regression (MLR), additive non-linear regression (ANLR), multiplicative non-linear regression (MNLR), and generalized regression neural network (GRNN) models are compared with two semi-empirical regression models from the literature. The results indicate that the GRNN model is the best model among the selected models. These results are also linked to the nature of the hydraulic jump and the turbulent behavior of the phenomenon, which masks the experimental results with outliers.Öğe Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition(Springer, 2019) Vaheddoost, Babak; Aksoy, HafzullahFrequency domain analysis using an additive decomposition method is proposed to reconstruct the missing hydrometeorological data of selected sites in Lake Urmia basin in Iran. Precipitation, evaporation, streamflow and groundwater time series are used for this aim. Trends, within- and multi-year cycles, and randomness are taken into account to reconstruct each of the time series for which models are developed, calibrated and validated separately. Statistical similarity between the observed and reconstructed time series is checked. Statistical characteristics including the average, standard deviation, skewness, and the first-order autocorrelation coefficient are well preserved at the reconstructed time series. A conceptual water budget model is also established to check for the consistency between the reconstructed and the observed datasets. The water budget model is taken as a quantitative way to confirm that the frequency domain analysis using the additive decomposition is an effective method for the reconstruction of the missing hydrometeorological data based on the case study performed for the Lake Urmia basin in Iran.Öğe Regressive-stochastic models for predicting water level in Lake Urmia(Taylor and Francis Ltd., 2021) Vaheddoost, Babak; Aksoy, HafzullahThis study develops a set of models to investigate the water budget of Lake Urmia in Iran, a permanent hypersaline lake that has suffered a declining water level since the late 1990s. The models are of the regressive-stochastic type, a combination of multilinear regression and autoregressive integrated moving average stochastic models. The multilinear regression models were used to construct the core of the relationship of lake water level to streamflow, precipitation, evaporation and groundwater depth. Afterward, stochastic models were used to generate data for each independent variable to estimate the oscillation in the lake water depth. Several criteria were used to compare the performance of the models in the aggregated and disaggregated cases with which the pre- and post-encroachment periods are considered, respectively. The regressive-stochastic models are found to be competitive with the existing models developed so far for Lake Urmia water level.Öğe A Spatiotemporal Classification of the Peruvian Precipitations Between 1990 and 2015(Springer Basel Ag, 2020) Vaheddoost, BabakPrecipitation and its variations have great importance in water resource management and sustainable development. In this study, the Peruvian precipitations between January 1990 to October 2015, were used. The precipitations were classified under spatial, temporal, and spatiotemporal classes. For this aim, properties of the precipitation time series including the monthly mean, monthly standard deviation, and principal components at monthly and annual scale were evaluated. Results were projected on a map using the Kriging method. Later, the double mass curves of the monthly precipitation time series were used to classify the temporal changes in the precipitations. Thereafter, the Spearman rank-order correlation was used to evaluate the spatiotemporal changes in monthly and annual precipitation time series by projected t-values on the Peruvian map. Finally, precipitations time series were plotted against Koppen-Geiger climate class of each station and several large scale oscillations namely North Atlantic Oscillation (NAO), El Nino/Southern Oscillation (ENSO), Atlantic Multi-decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO) simultaneously. It was concluded that there are at least three major climatic regions in the country. Spatial classes, depicts that the Andes Ranges is a major role player in the climate of the country while the ENSO and PDO are the main drivers of the precipitation extremes. Results also indicated to an ascending changes in the amount of precipitation from west to east, while a descending changes were observed at Amazon forest near San Ramon.Öğe A spatiotemporal teleconnection study between Peruvian precipitation and oceanic oscillations(Pergamon-Elsevier Science Ltd, 2020) Mohammadi, Babak; Vaheddoost, Babak; Mehr, Ali DanandehLarge-scale oceanic oscillations and their teleconnections with meteorological events are of great importance in macro-scale climatic studies. In this regard, this study investigates the spatiotemporal teleconnections between four oceanic oscillations, namely North Atlantic Oscillation (NAO), El Nino/Southern Oscillation (ENSO), Atlantic Multi-Decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO), against Peruvian precipitation patterns during the past 25 years (i.e., 1990-2015). For this purpose, variation in the precipitation pattern at monthly and annual scales as well as the Standardized Precipitation Index (SPI) time series at 1-, 3-, 12-, and 48-month time scales were evaluated at 10 meteorology stations across Peru. Pearson's correlation coefficient and mutual information between the oceanic oscillations and precipitation-born signals were calculated and spatially interpolated using the Kriging method. The results indicated the presence of three major climatic regions in the country. The NAO has the largest correlation with the monthly precipitation. However, the ENSO was found as the main climate driver of extremely wet and extremely dry conditions in the country. The results also demonstrated that the PDO has a higher impact on the annual precipitation pattern, particularly in the southern and eastern parts of the country.Öğe A stochastic approach for the assessment of suspended sediment concentration at the Upper Rhone River basin, Switzerland(Springer, 2022) Vaheddoost, Babak; Vazifehkhah, Saeed; Safari, Mir Jafar SadeghThis study addresses the link between suspended sediment concentration, precipitation, streamflow, and direct runoff components. This is important since suspended sediment concentration in the streamflow has invaluable importance in the management of the river basin. For this, the daily streamflow time series in five consecutive stations at Upper Rhone River Basin, a relatively large basin in the Alpine region of Switzerland, daily precipitation at one station, and the twice a week suspended sediment concentration records at the most downstream station between January 1981 and October 2020 are used. Initially, the base flow and the direct runoff associated with streamflow time series are obtained using the sliding interval method. Elasticity analyses between streamflow and suspended sediment concentration together with correlation, autocorrelation, partial autocorrelation, stationarity, and homogeneity are examined by the Augmented Dickey-Fuller and Pettitt's tests, respectively. Then, various stochastic scenarios are generated using the autoregressive moving average exogenous method (ARMAX). It is concluded that the precipitation and direct runoff have fewer effects on the suspended sediment concentration at downstream of the river. Hence, the cumulative effect of the glacier or snowmelt and channel erosion may exceed the effect of rain blown washouts on the suspended sediment concentration at the Port du Scex station. It is found that the ARMAX model results are satisfactory and can be suggested for further application.Öğe Temporal dynamics of monthly evaporation in Lake Urmia(Springer Wien, 2019) Vaheddoost, Babak; Kocak, KasimAs a UNESCO biosphere, Lake Urmia is a shallow hypersaline lake which is facing a rapid water surface degradation. Evaporation from the surface of the Lake, as a physical process which accelerates the Lake's degradation, was evaluated using chaos theory. Seven hydrometeorological stations scattered around the Lake were selected, and a 40-year time span between October 1974 and September 2014 was used at each station. Missing data in time series was removed and the whole time series was tested for consistency, randomness, and presence of trend. Since evaporation at each station was measured by means of class A evaporation pan, time series at each station was multiplied by a pan coefficient to incorporate the effect of saline water and free water surface environment simultaneously. Measurement errors arising from assumption of zero evaporation in winter were removed from the time series using locally weighted scatterplot smoothing method after which unification of time series into a single time series is achieved. Results of the data transformation and information loss were monitored by means of auto-correlation, partial-auto-correlation, mutual information, power spectrum, false nearest neighbor, and correlation dimension. A local prediction method is then used to capture the temporal dynamics of the evaporation with consideration of an appropriate time delay and embedding dimension. Finally, the representative model was projected on a 3-dimensional phase space to evaluate the temporal dynamics of the evaporation. Results indicate that the chaotic approach shows accurate predictions in advance.Öğe Three dimensional flow simulation over a sharp-crested V-Notch weir(Elsevier Sci Ltd, 2020) Saadatnejadgharahassanlou, Hamid; Zeynali, Rasoul Ilkhanipour; Gharehbaghi, Amin; Mehdizadeh, Saeid; Vaheddoost, BabakThin-plate weirs are widely used to monitor the flow rate in open channels. Thereby, three dimensional (3D) modeling of the flow over a weir in an open channel can be considered as one of the main topics in hydraulic science. In this study, the flow over a sharp-crested v-notch weir (SCVW) is simulated by a 3D numerical model. Laboratory experiments were conducted to monitor and measure the behavior of the SCVW in practice. Finally, the simulated velocity distributions, water surface profiles, and hydraulic jump were compared with those of the experimental data. Due to the turbulent nature of the flow over the SCVW, a Reynolds stress model (RSM) and three types of the k-e turbulence models with the fractional volume of fluid technique (VOF) were used in the analysis. In this respect, the two-phase solution method and dense mesh were used in generating the simulation domain. Results indicated that the RSM exhibited higher accuracy in defining the velocity distribution, complex flow pattern, and predicting the hydraulic jump formation downstream of the SCVW.Öğe Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm(Springer, 2021) Sales, Ali Kozekalani; Gul, Enes; Safari, Mir Jafar Sadegh; Ghodrat Gharehbagh, Hadi; Vaheddoost, BabakLake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent groundwater discharge, and hydrogeological situations. By incorporating the streamflow, groundwater, evaporation, and precipitation parameters into the models, Urmia lake water depth is simulated in the current study. For this, 40 years of streamflow and groundwater recorded data, respectively collected from 18 and 9 stations, are utilized together with evaporation and precipitation data from 7 meteorological stations. Extreme learning machine (ELM) is hybridized with four different optimizers, namely artificial bee colony (ABC), ant colony optimization for continuous domains (ACOR), whale optimization algorithm (WOA), and improved grey wolf optimizer (IGWO). In the analysis, 13 various scenarios with multiple input combinations are used to train and test the employed models. The best scenarios are then opted based on the performance metrics which are applied to assess the accuracy of the methods. According to the results, the hybrid ELM-IGWO shows better performance compared to the ELM-ABC, ELM-ACOR, and ELM-WOA approaches. Results indicate that the groundwater and persistence of the lake water depth have effective roles in models while incorporating higher number of variables can lower the performance of the models. Statistical analysis showed a 62% improvement in the performance of ELM-IGWO in comparison to the ELM-WOA with regard to the root mean square error. The promising outcomes obtained in this study may encourage the application of the utilized algorithms for modeling alternative hydrological problems.
